Projections are key to formulate the expectations regarding future cash flows to have the liquidity at the desired level. Unfortunatly there is no way to predict the future reliably.
However the Apliqo LP PM solution offers the ability to model best-practices for projection based on the solid data foundation. The solution supports the standard “Takahashi - Alexander Model”.
How it works
For each investment a is applied. Either manually by the user or automatically based on the Apliqo LP PM projection model.
The Apliqo LP PM projection model estimates
the cumulative future distributions,
the quarterly NAV net gains and the
Quarterly cash-flows: distributions and capital calls.
the model leverages the Takahashi-Alexander Model (a.k.a. Yale Model) and uses fund data with a minimum of 10 years (see below for more details). The model takes the fund strategy (e.g. Buyout) into consideration
For each investment a assumed projection curve is applied
Distributions
Contributions
Net Gain curve Scenario.
Apliqo LP PM supports custom statistical models. User generated models can be loaded into the system.
The model
The projection model takes into consideration the available reported data (dark green) and the estimations by the LP (light green). The model applies Apliqo proprietary projections methods (yellow arrows) to calculate the expected NAV uplift and the quarterly distributions per fund.
Legend:
more details to the Apliqo LP PM cash flow projection model can be requested at
info (at) apliqo.com
How are the projections calculated?
The model estimates for all Asset Classes and Strategies different contribution and distributions per quarter, based on the historic performance of the these asset groups.
Available Strategies are: Venture (VC), Buyout (BO), Growth (GROWTH), Infrastructure (INFRA), Real Estate (RE), Private Debt (PD)
The Portfolio manager applies for each fund a percentile estimate on how the fund will call capital and pay distributions.
Apliqo maintains a forecast model for these asset classes and strategies per vintage year. As an example a contribution curve for a Buyout fund rated as 75 % percentile could look like this.
Disclaimer:
Limited Reliability of Algorithms: The Software utilizes various algorithms to generate projections. While Apliqo have made reasonable efforts to ensure the accuracy and reliability of these algorithms, it is important to note that they may not always produce entirely reliable results. Factors such as input data quality, system configurations, and other variables can impact the accuracy of the projections. Therefore, Apliqo cannot guarantee the absolute reliability or correctness of the algorithmic outputs.
No Liability for Inaccurate Results: Apliqo and any other associated parties shall not be held liable for any inaccuracies, errors, or damages resulting from the use of the Software or its projections. This includes, but is not limited to, financial losses, disruptions in business operations, or any other direct or indirect damages arising from reliance on the projected data.
User's Responsibility for Validation: It is the user's responsibility to independently validate the projections generated by the Software and exercise caution when relying on them. The user should exercise their professional judgment and consider multiple sources of information before making any decisions based on the projections obtained through the Software.
The quarterly projected NAV net gain is calculated on the
Net gain curve
The model supports 3 different net gain curves. The curves differ from the speed, how quickly the fund reaches its total projected NAV uplift.
Where to maintain the projections?
The projections are maintained in the Data Management - Fund setup - Fund projection assumptions
Where can it be found in the solution
What is the basis of the projection assumptions?
The statistical projection assumptions for the Apliqo LP PM Projection Model are based on cashflows and valuations from over 1’200 funds, with a minimum of 10 years of history.
Buyout funds: 816
Growth funds: 121
VC funds: 324
With the vintages between 1985 and 2014 the value development the model covers different economical cycles.
Dotcom-Bubble (1995-2000)
Y2K recession (2000-2002)
Global Financial Crisis (2007-2009)
COVID 19 (2020-2021)
To exclude the younger funds does minimize the risk to base the projection on inflated unrealized inflated valuations during a period of very low interest rates.
Table: breakdown of Funds per vintage and strategy
BO | Growth | VC | |
Total | 816 | 121 | 324 |
1985 | 2 | ||
1986 | 1 | 2 | |
1987 | 4 | 2 | |
1988 | 5 | 1 | |
1989 | 1 | ||
1990 | 1 | 4 | |
1991 | 3 | 1 | |
1992 | 5 | 2 | 5 |
1993 | 7 | 1 | 5 |
1994 | 16 | 6 | |
1995 | 13 | 7 | |
1996 | 17 | 2 | 8 |
1997 | 20 | 12 | |
1998 | 36 | 1 | 16 |
1999 | 31 | 3 | 19 |
2000 | 33 | 5 | 37 |
2001 | 21 | 3 | 23 |
2002 | 22 | 2 | 15 |
2003 | 28 | 11 | |
2004 | 30 | 3 | 10 |
2005 | 58 | 6 | 14 |
2006 | 66 | 10 | 20 |
2007 | 67 | 13 | 26 |
2008 | 62 | 9 | 19 |
2009 | 27 | 6 | 7 |
2010 | 36 | 7 | 10 |
2011 | 43 | 14 | 13 |
2012 | 45 | 11 | 10 |
2013 | 60 | 9 | 11 |
2014 | 57 | 14 | 9 |